Peer-assessment and grading of open answers in a web-based e-learning setting

Grading the answers given to open ended questions (and questionnaires) is a rather heavy task for teachers and lecturers. Several techniques have been used to automatically analyze the student´s essays, but natural language processing is not yet ready to completely solve the task. We propose a solution, for the automated support to answers grading, where the students´ peer assessment is checked against the teacher´s marking, while such teacher´s marking has to be performed on only a subset of the answers (so relieving the teacher by at least a part of the marking task). Our aim is twofold: 1) to make grading more effective by propagating the teacher´s assessments through the network of peer assessments (so to maintain a progressively improved evaluations of the peer assessments precision and make them more likely to be used for final grading); 2) to suggest iteratively the teacher with the next most informative essays to grade (respect to the network, that is one of the answers whose teacher´s mark will inject most information into the network and make the system better able to deduce the rest of the grades. To this aim we have used a very simple Bayesian model of peers and peer assessment, and we have built a web-based system to support the teacher. We show the present state of the model and its implementation. Moreover, we show, through a simulation protocol, how the system can support the teacher, obtaining a reasonably good correction with just a subset of the whole grades.